Power distribution network synchronous phasor measurement method, system and device and storage medium

Abstract

The invention discloses a power distribution network synchronous phasor measurement method, system and device and a storage medium. The method comprises the steps of collecting power data of a power distribution network, and preprocessing the power data to obtain first data; calculating the amplitude spectrum and the power spectrum density of the first data, defining a state vector based on a calculation result, and constructing a state space model; and calculating a state vector estimation value based on the state space model, constructing a probability distribution model, and taking a final result output based on the probability distribution model as a basis for synchronous phasor measurement and oscillation monitoring. According to the method, energy calculation is carried out on the oscillation source characteristics in the power distribution network, the measurement precision and the real-time performance are remarkably improved, the dynamic characteristics of the system are estimated through the state space model in combination with real-time power data, the system state is accurately estimated and predicted, the calculation process of oscillation source positioning is optimized, and the positioning accuracy of the oscillation source is improved. A measurement model can be dynamically adjusted, and the measurement precision and the system response speed are further improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power distribution network synchronous phasor measurement, and in particular to a power distribution network synchronous phasor measurement method, system, device and storage medium. BACKGROUND

[0002] With the continuous expansion of the scale of the power distribution network and the increasing complexity of the operation mode of the power system, the stability and reliability of the power system have become key issues in the operation and management of the power system. As an advanced power system monitoring method, the synchronous phasor measurement technology has been widely used in real-time state monitoring, fault detection, oscillation source analysis and other fields of the power system. Although the synchronous phasor measurement technology is widely used in the power system, the existing technology still faces some significant problems. First, the traditional synchronous phasor measurement method has high requirements for data quality, especially in the power distribution network, where the nodes are widely distributed and are affected by environmental factors, and there is much power data noise. Secondly, the existing method is mostly based on the Fourier transform and the fixed threshold algorithm, and cannot effectively dynamically adjust the measurement method to adapt to the changing power system conditions. In particular, in the detection and positioning of the oscillation source, there is a lack of effective dynamic response mechanism. More importantly, the calculation model of the existing method generally relies on static state estimation, and lacks real-time response and accurate prediction of the dynamic characteristics of the power system. SUMMARY

[0003] In view of the above existing problems, the present application is proposed. Therefore, the present application provides a power distribution network synchronous phasor measurement method, system, device and storage medium to solve the problems of low calculation accuracy and poor real-time performance in the key links of oscillation source positioning and energy calculation of the existing synchronous phasor measurement method.

[0004] To solve the above technical problems, the present application provides the following technical solutions:

[0005] In a first aspect, the present application embodiment provides a power distribution network synchronous phasor measurement method, comprising:

[0006] Collecting power distribution network power data and preprocessing the power data to obtain first data;

[0007] Calculating the amplitude spectrum and power spectral density of the first data, defining a state vector based on the calculation result, and constructing a state space model;

[0008] Calculating the state vector estimate value based on the state space model, constructing a probability distribution model, and taking the final result output by the probability distribution model as the basis for synchronous phasor measurement and oscillation monitoring.

[0009] As a preferred scheme of the power distribution network synchronous phasor measurement method of the present application, wherein: the preprocessing of the power data to obtain the first data comprises:

[0010] The DC component in the power data is removed using a bandpass filter, and high-frequency noise is removed using wavelet denoising.

[0011] The missing values ​​in the denoised power data are filled in, the filled power data is optimized, and the optimized power data is normalized to obtain the first data.

[0012] As a preferred embodiment of the distribution network synchronous phasor measurement method of the present invention, the calculation of the amplitude spectrum and power spectral density of the first data includes: performing a fast Fourier transform on the first data;

[0013] The time-domain signal is converted into a frequency-domain signal, and the complex spectrum is extracted based on the converted frequency-domain signal to calculate the amplitude spectrum of the frequency components. The power spectral density is calculated based on the amplitude spectrum, and the energy of the oscillation source within the frequency range is calculated using a frequency-domain energy calculation method.

[0014] As a preferred embodiment of the distribution network synchronization phasor measurement method described in this invention, the method for constructing a state space model based on the calculation results includes: defining the first data as a state quantity and defining a state vector according to the state quantity; extracting the input quantity from the first data and constructing an input vector after calculating the correlation between the input quantity and the state quantity using the Pearson correlation coefficient.

[0015] Based on the obtained state vector and input vector Construct the state-space equations, which include input and output equations:

[0016] ,

[0017] ,

[0018] in, State vector at time t The derivative of , where A represents the state transition matrix and B represents the control input matrix. The input vector representing time t, Let C represent the output vector at time t, C represent the output matrix, and D represent the transmission matrix.

[0019] As a preferred embodiment of the distribution network synchronization phasor measurement method of the present invention, the calculation of the state vector estimate based on the state space model includes: combining the input equation, the output equation and the gain matrix, and calculating the state vector estimate.

[0020] Using the pole placement method and defining the gain matrix L based on the first data, the oscillation source energy is mapped to a vector space to obtain the oscillation source energy vector. This energy vector is then fused with the calculated state vector estimate to obtain the fused state vector.

[0021] ,

[0022] in, This represents the fused state vector at time t. This indicates the transpose operation. The weight vector represents the estimated state vector. The weight vector represents the energy vector of the oscillation source.

[0023] The beneficial effect of this preferred technical solution is that by integrating state estimation and oscillation energy information, the ability to locate oscillation sources can be enhanced.

[0024] As a preferred embodiment of the distribution network synchronization phasor measurement method described in this invention, the construction of the probability distribution model includes: converting the fused state vector into a scalar G, and using the converted scalar G as the observation data D; constructing the probability distribution model of the oscillation source using the observation data D, and defining the objective function:

[0025] ,

[0026] in, Indicates that under given parameters The following observation data The probability, H represents the model parameters, and H represents the number of observation points. This represents the actual scalar value at the i-th observation point. Indicates in the parameter The predicted scalar value for the i-th observation point. Let represent the variance of the i-th observation point.

[0027] As a preferred embodiment of the distribution network synchronization phasor measurement method described in this invention, the final result output based on the probability distribution model serves as the basis for synchronization phasor measurement and oscillation monitoring, including:

[0028] Set the initial position and learning rate, train the probability distribution model, and when the iterative convergence condition is met, output the optimal parameters to optimize the probability distribution model;

[0029] The observed data is input into the optimized probability distribution model, and the state vector enhancement value and oscillation source energy enhancement value are output through the probability distribution model.

[0030] The state vector enhancement value and the oscillation source energy enhancement value are transformed to obtain scalar values ​​for the state vector enhancement value and scalar values ​​for the oscillation source energy enhancement value, respectively.

[0031] The scalar values ​​of the state vector enhancement value, the scalar values ​​of the oscillation source energy enhancement value, and the scalar values ​​of the state vector transformation are fused to obtain a comprehensive scalar value;

[0032] Based on the obtained scalar values, wavelet transform analysis is used to analyze and obtain the characteristics of the oscillation source. The obtained characteristics of the oscillation source are used as the basis for monitoring the oscillation source of synchronous phasor measurement.

[0033] The beneficial effect of this preferred technical solution is that the comprehensive scalar fusion mechanism ensures stable output in complex environments.

[0034] Secondly, the present invention provides a power distribution network synchronization phasor measurement system, comprising:

[0035] The data acquisition and processing module is used to acquire power data from the power distribution network and preprocess the power data to obtain first data.

[0036] The calculation module is used to calculate the amplitude spectrum and power spectral density of the first data, define a state vector based on the calculation results, and construct a state space model.

[0037] The optimization module is used to calculate the estimated value of the state vector based on the state space model and construct a probability distribution model. The final result output by the probability distribution model serves as the basis for synchronous phasor measurement and oscillation monitoring.

[0038] Thirdly, the present invention provides an electronic device, comprising:

[0039] Memory and processor;

[0040] The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions, which, when executed by the processor, implement the steps of the power distribution network synchronization phasor measurement method.

[0041] Fourthly, the present invention provides a computer-readable storage medium storing computer-executable instructions that, when executed by a processor, implement the steps of the power distribution network synchronization phasor measurement method.

[0042] Compared with the prior art, the beneficial effects of the present invention are as follows: The present invention significantly improves the measurement accuracy and real-time performance by performing energy calculations on the characteristics of oscillation sources in the distribution network. Furthermore, by combining the state-space model with real-time power data to estimate the dynamic characteristics of the system, the system state can be accurately estimated and predicted, optimizing the calculation process for oscillation source location. Moreover, the Bayesian optimization method can dynamically adjust the measurement model, thereby further improving the measurement accuracy and system response speed. Attached Figure Description

[0043] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Wherein:

[0044] Figure 1 This is a schematic diagram of the method flow for a power distribution network synchronization phasor measurement method according to an embodiment of the present invention. Detailed Implementation

[0045] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0046] Example 1, referring to Figure 1 As one embodiment of the present invention, this embodiment provides a method for measuring the synchronization phasor of a power distribution network, comprising:

[0047] S100: Collect power data from the power distribution network and preprocess the power data to obtain the first data;

[0048] S200: Calculate the amplitude spectrum and power spectral density of the first data, define the state vector based on the calculation results, and construct the state space model;

[0049] S300: Calculates state vector estimates based on the state-space model and constructs a probability distribution model. The final result output by the probability distribution model serves as the basis for synchronous phasor measurement and oscillation monitoring.

[0050] It should be noted that traditional synchronous phasor measurement methods have high requirements for data quality, especially in distribution networks where nodes are widely distributed and power data is subject to significant noise due to environmental factors. Secondly, most existing methods are based on Fourier transforms and fixed threshold algorithms, failing to effectively dynamically adjust the measurement method to adapt to changing power system conditions, particularly in the detection and location of oscillation sources, where an effective dynamic response mechanism is lacking. More importantly, the computational models of existing methods generally rely on static state estimation, lacking real-time response and accurate prediction of the dynamic characteristics of the power system. This invention significantly improves measurement accuracy and real-time performance by calculating the energy characteristics of oscillation sources in distribution networks. Furthermore, by combining a state-space model with real-time power data to estimate the dynamic characteristics of the system, and then accurately estimating and predicting the system state, the calculation process for oscillation source location is optimized. Moreover, the Bayesian optimization method can dynamically adjust the measurement model, further improving measurement accuracy and system response speed.

[0051] In this embodiment of the invention, the preprocessing of power data to obtain the first data in step S100 includes:

[0052] The DC component in power data is removed based on a bandpass filter, and high-frequency noise is removed based on wavelet denoising.

[0053] Missing values ​​in the denoised power data are filled in, the filled power data is optimized, and the optimized power data is normalized to obtain the first data.

[0054] Specifically, calculate the amplitude spectrum of the frequency components. :

[0055] ,

[0056] in, Representing the spectrum of complex numbers The real part, Representing the spectrum of complex numbers The imaginary part;

[0057] Calculate power spectral density :

[0058] ,

[0059] in, This indicates the frequency resolution; the frequency resolution can be obtained using the sliding window technique. ;

[0060] Based on the obtained power spectral density Frequency domain energy calculation method is used to calculate the frequency range. Energy calculation of the internal oscillation source:

[0061] ,

[0062] in, Indicates frequency range Internal oscillation source energy, and These represent the lower and upper limits of the frequency range, respectively.

[0063] It should be noted that by calculating the amplitude spectrum and power spectral density of power data, the dynamic monitoring capability and fault diagnosis accuracy of the distribution network are significantly improved. Furthermore, by using the fast Fourier transform to convert the time-domain signal into a frequency-domain signal, the frequency components in the power system can be efficiently extracted, the oscillation source can be accurately located, and its energy distribution can be calculated. By calculating the amplitude spectrum and power spectral density, not only can the frequency characteristics in the signal be revealed, but also the dynamic disturbance sources in the power system can be effectively identified, thereby providing key data support for system stability analysis and fault early warning. This step enables the present invention to promote the adaptive control and optimized scheduling of smart grids.

[0064] In an optional embodiment, smart sensors are deployed at the nodes of the distribution network, and real-time power data is collected based on the deployed smart sensors; the DC component in the power data is removed by using a bandpass filter, and high-frequency noise is removed by using wavelet denoising; based on the denoised power data, missing data is filled by linear interpolation, and the filled power data is optimized by Hamming window; the optimized power data is normalized using a normalization method.

[0065] It should be noted that the deployment of intelligent sensors ensured the accurate acquisition of real-time power data, providing a reliable data foundation for subsequent analysis. The bandpass filter removed the DC component, effectively eliminating offset errors and ensuring signal accuracy. Furthermore, wavelet denoising was used to remove high-frequency noise, maintaining high-quality power data even in complex environments and avoiding noise interference. Linear interpolation filled in missing data, ensuring data integrity and avoiding errors caused by missing data. Moreover, Hamming window optimization improved the frequency domain resolution of the signal, supporting the precise location of the oscillation source. Normalization eliminated the influence between different units, ensuring data consistency and the stability of subsequent processing. The synergistic effect of these steps improved the accuracy of data processing.

[0066] In this embodiment of the invention, calculating the amplitude spectrum and power spectral density of the first data in step S200 includes performing a fast Fourier transform on the first data.

[0067] The time-domain signal is converted into a frequency-domain signal, and the complex spectrum is extracted based on the converted frequency-domain signal to calculate the amplitude spectrum of the frequency components. The power spectral density is calculated based on the amplitude spectrum, and the energy of the oscillating source within the frequency range is calculated using the frequency-domain energy calculation method.

[0068] Specifically, the state vector is defined based on the state variables:

[0069]

[0070] in, The state vector representing time t. This represents the nth state variable at time t.

[0071] In this embodiment of the invention, step S200, which defines a state vector based on the calculation results and constructs a state space model, includes: defining the first data as a state quantity and defining a state vector according to the state quantity; extracting the input quantity from the first data and constructing an input vector after calculating the correlation between the input quantity and the state quantity using the Pearson correlation coefficient;

[0072] Based on the obtained state vector and input vector Construct the state-space equations, which include input and output equations:

[0073] ,

[0074] ,

[0075] in, State vector at time t The derivative of , where A represents the state transition matrix and B represents the control input matrix. The input vector representing time t, Let C represent the output vector at time t, C represent the output matrix, and D represent the transmission matrix.

[0076] It should be noted that the state transition matrix, control input matrix, output matrix, and transfer matrix can be extracted from the input-output data using the least squares method.

[0077] In this embodiment of the invention, step S300, which calculates the state vector estimate based on the state space model, includes: combining the input equation, the output equation, and the gain matrix, and then calculating the state vector estimate.

[0078] Using the pole placement method and defining the gain matrix L based on the first data, the oscillation source energy is mapped to a vector space to obtain the oscillation source energy vector. This energy vector is then fused with the calculated state vector estimate to obtain the fused state vector.

[0079]

[0080] in, This represents the fused state vector at time t. This indicates the transpose operation. The weight vector represents the estimated state vector. The weight vector represents the energy vector of the oscillation source.

[0081] For example, the input equation, output equation, and gain matrix L are combined, and the state vector estimate is calculated using the Luenberger observer method: the oscillation source energy is mapped to the vector space by using an embedding technique to obtain the oscillation source energy vector; the oscillation source energy vector is fused with the calculated state vector estimate using a weighted average method to obtain the fused state vector.

[0082] It should be noted that defining normalized power data as state variables eliminates scale differences between different state variables, providing a unified data platform for subsequent state vector construction and ensuring the accuracy and stability of the analysis in this invention. Secondly, by extracting input variables from the data through correlation analysis and calculating the correlation between input variables and state variables, it ensures that the input vector accurately reflects the dynamic characteristics of the power system, providing a solid foundation for subsequent state-space equations. Furthermore, the construction of state-space equations and the extraction of key system parameters from input and output data using the least squares method provide theoretical support for the state estimation and dynamic prediction of this invention. Finally, the Luenberger observer method is applied to calculate the state vector estimation... The estimation method can accurately estimate the state by relying on input and output data when certain state variables cannot be directly measured, thereby improving the state prediction accuracy and dynamic response capability of the present invention. Especially in complex power systems, it can respond to changes quickly and accurately, enhancing the stability and reliability of the present invention. In addition, the present invention also combines the mapping and fusion technology of oscillation source energy, mapping the oscillation source energy to the state vector space, and fusing it with the state vector estimate through a weighted average method, further improving the monitoring capability of dynamic disturbance sources of the present invention. The input vector not only serves as a description of the system state, but also as an intervention means for dynamic adjustment and optimization, further improving the system's scheduling efficiency and fault emergency response capability.

[0083] In this embodiment of the invention, step S300, constructing the probability distribution model, includes: converting the fused state vector into a scalar G, and using the converted scalar G as the observation data D; constructing the probability distribution model of the oscillation source using the observation data D, and defining the objective function:

[0084] ,

[0085] in, Indicates that under given parameters The following observation data The probability, H represents the model parameters, and H represents the number of observation points. This represents the actual scalar value at the i-th observation point. Indicates in the parameter The predicted scalar value for the i-th observation point. Let represent the variance of the i-th observation point.

[0086] In this embodiment of the invention, the final result output based on the probability distribution model in step S300 serves as the basis for synchronous phasor measurement and oscillation monitoring, including:

[0087] Set the initial position and learning rate, train the probability distribution model, and when the iterative convergence condition is met, output the optimal parameters to optimize the probability distribution model;

[0088] The observed data is input into the optimized probability distribution model, and the state vector enhancement value and oscillation source energy enhancement value are output through the probability distribution model.

[0089] The state vector enhancement value and the oscillation source energy enhancement value are transformed to obtain scalar values ​​for the state vector enhancement value and scalar values ​​for the oscillation source energy enhancement value, respectively.

[0090] The scalar values ​​of the state vector enhancement value, the scalar values ​​of the oscillation source energy enhancement value, and the scalar values ​​of the state vector transformation are fused to obtain a comprehensive scalar value;

[0091] Based on the obtained scalar values, wavelet transform analysis is used to analyze and obtain the characteristics of the oscillation source. The obtained characteristics of the oscillation source are used as the basis for monitoring the oscillation source of synchronous phasor measurement.

[0092] It should be noted that by using the norm method to transform the multidimensional state vector into a scalar form, the data processing is simplified while maintaining an accurate description of the dynamic characteristics. Furthermore, the combination of Bayesian optimization and gradient descent allows for adaptive adjustment of model parameters, continuously optimizing the oscillation source monitoring effect. This enables rapid response and accurate estimation when the power system operating environment changes. Secondly, the weighted fusion method integrates multiple indicators of the state vector and oscillation source energy, further enhancing the system's ability to accurately track oscillation sources and improving the globality, reliability, and interpretability of the results. It also provides unified indicators, simplifying subsequent analysis. Finally, wavelet transform is used for time-frequency analysis of the oscillation source characteristics, enabling real-time capture of its changing trends and frequency characteristics, providing a detailed description of the oscillation source's dynamic characteristics. This step ensures the efficiency and accuracy of oscillation source monitoring and significantly improves the system's adaptability in dynamic environments.

[0093] In an optional embodiment, the final result obtained in step S300 can be stored in a database and displayed through a visual interface;

[0094] Specifically, storing the final results in the database includes: storing the comprehensive scalar values ​​in JSON format; storing the results after further fusion, the original data, and the parameters of the probability distribution model; and setting data table fields and recording the results of each calculation according to the actual location.

[0095] After storage, the script stores the results as JSON files after each calculation, categorizing them by date and region.

[0096] It should be noted that by using JSON format to store the calculated comprehensive scalar values, efficient storage and management of power system oscillation source monitoring results are ensured. The stored content includes the fused calculation results, raw data, and probability distribution model parameters, facilitating subsequent querying and analysis. Furthermore, by reasonably setting data table fields and recording each calculation result according to actual location, the structured and efficient storage of data is improved. After each calculation, the results will be automatically generated into a JSON file by a script and stored according to date and region, achieving flexible data management and convenient access.

[0097] Furthermore, the visualization is presented through the following methods: using the data visualization tool Plotly to create dynamic charts; using a node diagram with the distribution network topology as a background to show the location of the oscillation source, the size of the nodes, and the color reflecting the strength of the oscillation source energy; displaying the energy distribution of each node through heat maps and color scale diagrams; displaying the changing trend of frequency components through dynamic charts, and annotating and displaying the peak frequency and its corresponding oscillation intensity; and saving the generated dynamic charts as HTML files.

[0098] It should be noted that by using the Plotly data visualization tool, the location, energy distribution, and frequency change trends of oscillation sources in the distribution network can be intuitively displayed in dynamic charts. The node diagram shows the location and energy intensity of the oscillation source, while the heat map and color scale diagram show the energy distribution of each node. This helps maintenance personnel quickly identify potential fault areas and energy anomaly areas. The dynamic frequency diagram can display the frequency changes and oscillation intensity of the oscillation source in real time, helping to analyze system response and predict potential problems. Furthermore, by saving the generated HTML format charts, the results are easily archived and shared.

[0099] Example 2, Example 2 is an embodiment of the present invention. This embodiment differs from the first embodiment in that it provides a power distribution network synchronization phasor measurement system, comprising:

[0100] The data acquisition and processing module is used to acquire power data from the power distribution network and preprocess the power data to obtain the first data.

[0101] The calculation module is used to calculate the amplitude spectrum and power spectral density of the first data, define the state vector based on the calculation results, and construct the state space model.

[0102] The optimization module is used to calculate the state vector estimate based on the state space model and construct the probability distribution model. The final result output by the probability distribution model serves as the basis for synchronous phasor measurement and oscillation monitoring.

[0103] Specifically, when the modules of the distribution network synchronization phasor measurement system in this embodiment are executed, they implement the steps of the distribution network synchronization phasor measurement method in Embodiment 1, for example:

[0104] In one implementation, the power distribution network synchronization phasor measurement system may perform the following steps:

[0105] The DC component in power data is removed based on a bandpass filter, and high-frequency noise is removed based on wavelet denoising.

[0106] Missing values ​​in the denoised power data are filled in, the filled power data is optimized, and the optimized power data is normalized to obtain the first data.

[0107] Perform a Fast Fourier Transform on the first data;

[0108] The time-domain signal is converted into a frequency-domain signal, and the complex spectrum is extracted based on the converted frequency-domain signal to calculate the amplitude spectrum of the frequency components. The power spectral density is calculated based on the amplitude spectrum, and the energy of the oscillating source within the frequency range is calculated using the frequency-domain energy calculation method.

[0109] The first data is defined as the state quantity, and the state vector is defined based on the state quantity; the input quantity is extracted from the first data, and the correlation between the input quantity and the state quantity is calculated using the Pearson correlation coefficient to construct the input vector;

[0110] Based on the obtained state vector and input vector Construct the state-space equations, which include input and output equations:

[0111] ,

[0112] ,

[0113] in, State vector at time t The derivative of , where A represents the state transition matrix and B represents the control input matrix. The input vector representing time t, Let C represent the output vector at time t, C represent the output matrix, and D represent the transmission matrix.

[0114] The input equation, output equation, and gain matrix are combined, and the state vector estimate is calculated.

[0115] Using the pole placement method and defining the gain matrix L based on the first data, the oscillation source energy is mapped to a vector space to obtain the oscillation source energy vector. This energy vector is then fused with the calculated state vector estimate to obtain the fused state vector.

[0116]

[0117] in, This represents the fused state vector at time t. This indicates the transpose operation. The weight vector represents the estimated state vector. The weight vector represents the energy vector of the oscillation source.

[0118] The fused state vector is transformed into a scalar G, and the transformed scalar G is used as the observation data D. Using the observation data D, a probability distribution model of the oscillation source is constructed, and the objective function is defined:

[0119] ,

[0120] in, Indicates that under given parameters The following observation data The probability, H represents the model parameters, and H represents the number of observation points. This represents the actual scalar value at the i-th observation point. Indicates in the parameter The predicted scalar value for the i-th observation point. Let represent the variance of the i-th observation point.

[0121] Set the initial position and learning rate, train the probability distribution model, and when the iterative convergence condition is met, output the optimal parameters to optimize the probability distribution model;

[0122] The observed data is input into the optimized probability distribution model, and the state vector enhancement value and oscillation source energy enhancement value are output through the probability distribution model.

[0123] The state vector enhancement value and the oscillation source energy enhancement value are transformed to obtain scalar values ​​for the state vector enhancement value and scalar values ​​for the oscillation source energy enhancement value, respectively.

[0124] The scalar values ​​of the state vector enhancement value, the scalar values ​​of the oscillation source energy enhancement value, and the scalar values ​​of the state vector transformation are fused to obtain a comprehensive scalar value;

[0125] Based on the obtained scalar values, wavelet transform analysis is used to analyze and obtain the characteristics of the oscillation source. The obtained characteristics of the oscillation source are used as the basis for monitoring the oscillation source of synchronous phasor measurement.

[0126] Example 3: This example provides an electronic device applicable to the distribution network synchronization phasor measurement method, including:

[0127] The system includes a memory and a processor. The memory stores computer-executable instructions, and the processor executes these instructions to implement the method for synchronous phasor measurement in a power distribution network as described in the above embodiments.

[0128] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements the method for implementing synchronous phasor measurement in a power distribution network as proposed in the above embodiments.

[0129] The storage medium proposed in this embodiment and the method for realizing synchronous phasor measurement in the distribution network proposed in the above embodiments belong to the same inventive concept. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0130] From the above description of the embodiments, those skilled in the art will clearly understand that the embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The solutions in the embodiments of this application can be implemented using various computer languages, such as the object-oriented programming language Java and the interpreted scripting language JavaScript.

[0131] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0132] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0133] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0134] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0135] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

[0136] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A power distribution grid synchrophasor measurement method, characterized by, The method comprises the following steps: Collecting power data of a power distribution network, and preprocessing the power data to obtain first data; Calculating the amplitude spectrum and power spectral density of the first data, defining a state vector based on the calculation results, and constructing a state space model; Based on the state space model, the state vector estimation value is calculated, and a probability distribution model is constructed, and the final result output by the probability distribution model is used as the basis for synchronous phasor measurement and oscillation monitoring.

2. The power grid synchrophasor measurement method of claim 1, wherein, The preprocessing of the power data to obtain the first data comprises: Removing the direct current component in the power data based on a band-pass filter, and removing high-frequency noise based on a wavelet denoising method; Filling the missing values of the denoised power data, optimizing the filled power data, and normalizing the optimized power data to obtain the first data.

3. The power grid synchrophasor measurement method of claim 2, wherein, The calculation of the amplitude spectrum and power spectral density of the first data comprises: performing fast Fourier transform on the first data; The time domain signal is converted into a frequency domain signal, and the complex frequency spectrum is extracted according to the converted frequency domain signal, the amplitude spectrum of the frequency component is calculated, the power spectral density is calculated based on the amplitude spectrum, and the energy of the oscillation source in the frequency range is calculated using the frequency energy calculation method.

4. The power grid synchrophasor measurement method of claim 3, wherein, Based on the calculation results, the state vector is defined, and the state space model is constructed, which comprises: defining the first data as a state quantity, and defining the state vector according to the state quantity; extracting the input quantity from the first data, and constructing the input vector after calculating the correlation between the input quantity and the state quantity using the Pearson correlation coefficient; From the resulting state vector and the input vector , a state space equation is constructed, the state space equation comprising an input equation and an output equation: ， ， wherein a state vector representing time t derivative of the state vector representing time t, A represents a state transition matrix, B represents a control input matrix, an input vector representing time t, an output vector representing time t, C represents an output matrix, D represents a transmission matrix.

5. The power grid synchrophasor measurement method of claim 4, wherein, Based on the state space model, the state vector estimation value is calculated, which comprises: combining the input equation, the output equation and the gain matrix, and calculating the state vector estimation value; The gain matrix L is defined by using the pole placement method and combining the first data, the oscillation source energy is mapped into the vector space to obtain the oscillation source energy vector, and the fusion of the oscillation source energy vector and the calculated state vector estimation value is obtained: , wherein denotes the state vector after fusion at time t, denotes a transposition operation, denotes a weight vector for the state vector estimate, denotes a weight vector for the oscillation source energy vector.

6. The power grid synchrophasor measurement method of claim 5, wherein, The construction of the probability distribution model comprises: converting the fused state vector into a scalar G, and taking the converted scalar G as observation data D; constructing a probability distribution model of the oscillation source through the observation data D, and defining an objective function: ， where denotes the probability of observing data under given parameters , denotes the model parameters, H denotes the number of observation points, denotes the actual scalar value of the i-th observation point, denotes the predicted scalar value of the i-th observation point under parameters , denotes the variance of the i-th observation point.

7. The power grid synchrophasor measurement method of claim 6, wherein, The final result output by the probability distribution model is used as the basis for synchronous phasor measurement and oscillation monitoring, which comprises: Setting an initial position and a learning rate, training the probability distribution model, and outputting an optimal parameter to optimize the probability distribution model when the iteration convergence condition is reached; The observation data is input into the optimized probability distribution model, and the state vector enhancement value and the oscillation source energy enhancement value are output by the probability distribution model; The state vector enhancement value and the oscillation source energy enhancement value are converted to obtain the scalar of the state vector enhancement value and the scalar of the oscillation source energy enhancement value, respectively; The scalar of the state vector enhancement value, the scalar of the oscillation source energy enhancement value and the converted scalar of the state vector are fused to obtain a comprehensive scalar value; Based on the obtained scalar value, the wavelet transform analysis method is used for analysis to obtain the oscillation source characteristics, and the obtained oscillation source characteristics are used as the basis for synchronous phasor measurement and oscillation source monitoring.

8. A power distribution grid synchrophasor measurement system applied to the method of any of claims 1-7, characterized in that, The method comprises the following steps: The data acquisition and processing module is configured to acquire power data of the power distribution network and to preprocess the power data to obtain first data. The calculation module is configured to calculate a magnitude spectrum and a power spectral density of the first data, define a state vector based on a calculation result, and construct a state space model. The optimization module is configured to calculate a state vector estimation value based on the state space model, construct a probability distribution model, and output a final result of the probability distribution model as a basis for the synchronous phasor measurement and the oscillation monitoring. 9.An electronic device, comprising: a memory and a processor; the memory is configured to store computer executable instructions, and the processor is configured to execute the computer executable instructions, and the computer executable instructions, when executed by the processor, implement the steps of the power distribution network synchronous phasor measurement method of any one of claims 1 to 7. 10.A computer readable storage medium storing computer executable instructions, and the computer executable instructions, when executed by a processor, implement the steps of the power distribution network synchronous phasor measurement method of any one of claims 1 to 7.

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